Introduced first in the [1]} minibatch discrimination can help GANs prevent mode collapse. We follow [2]} in their use of minibatch standard deviation as a simpler variant of the above technique to achieve the same goal. We first compute the standard deviation for each feature in each spatial location over the minibatch. We then average these estimates over all features and spatial locations to arrive at a single value. We replicate the value and concatenate it to all spatial locations and over the minibatch, yielding one additional (constant) feature map. This additional layer is added after every concatenation step.
